Overview

Dataset statistics

Number of variables11
Number of observations1048575
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory290.8 MiB
Average record size in memory290.8 B

Variable types

Numeric8
Categorical3

Alerts

CREATED_AT has a high cardinality: 1038198 distinct valuesHigh cardinality
UPDATED_AT has a high cardinality: 1046245 distinct valuesHigh cardinality
AMOUNT is highly overall correlated with FEES and 3 other fieldsHigh correlation
FEES is highly overall correlated with AMOUNT and 3 other fieldsHigh correlation
RETAILER_CUT is highly overall correlated with AMOUNT and 3 other fieldsHigh correlation
TOTAL_AMOUNT_INCLUDING_TAX is highly overall correlated with AMOUNT and 3 other fieldsHigh correlation
TOTAL_AMOUNT_PAID is highly overall correlated with AMOUNT and 3 other fieldsHigh correlation
STATUS is highly imbalanced (95.6%)Imbalance
AMOUNT is highly skewed (γ1 = 32.92559311)Skewed
FEES is highly skewed (γ1 = 82.21787189)Skewed
RETAILER_CUT is highly skewed (γ1 = 41.577119)Skewed
TOTAL_AMOUNT_INCLUDING_TAX is highly skewed (γ1 = 32.78087174)Skewed
TOTAL_AMOUNT_PAID is highly skewed (γ1 = 32.68072135)Skewed
CREATED_AT is uniformly distributedUniform
UPDATED_AT is uniformly distributedUniform
ID has unique valuesUnique

Reproduction

Analysis started2023-04-04 16:05:04.697113
Analysis finished2023-04-04 16:05:46.111171
Duration41.41 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

ID
Real number (ℝ)

Distinct1048575
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21187328
Minimum44446
Maximum57640096
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 MiB
2023-04-04T19:05:46.176246image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum44446
5-th percentile8251421.7
Q113539386
median20673482
Q325437632
95-th percentile41556269
Maximum57640096
Range57595650
Interquartile range (IQR)11898245

Descriptive statistics

Standard deviation10240261
Coefficient of variation (CV)0.48332007
Kurtosis0.071443264
Mean21187328
Median Absolute Deviation (MAD)6610793
Skewness0.76170538
Sum2.2216502 × 1013
Variance1.0486294 × 1014
MonotonicityNot monotonic
2023-04-04T19:05:46.297371image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21321668 1
 
< 0.1%
41615753 1
 
< 0.1%
41654058 1
 
< 0.1%
41654060 1
 
< 0.1%
51358960 1
 
< 0.1%
36193663 1
 
< 0.1%
36193692 1
 
< 0.1%
36193693 1
 
< 0.1%
36193704 1
 
< 0.1%
36193708 1
 
< 0.1%
Other values (1048565) 1048565
> 99.9%
ValueCountFrequency (%)
44446 1
< 0.1%
45307 1
< 0.1%
45811 1
< 0.1%
165403 1
< 0.1%
295226 1
< 0.1%
295484 1
< 0.1%
295717 1
< 0.1%
295853 1
< 0.1%
296154 1
< 0.1%
296410 1
< 0.1%
ValueCountFrequency (%)
57640096 1
< 0.1%
57640085 1
< 0.1%
57640080 1
< 0.1%
57640079 1
< 0.1%
57640036 1
< 0.1%
57640033 1
< 0.1%
57640024 1
< 0.1%
57639950 1
< 0.1%
51367348 1
< 0.1%
51367345 1
< 0.1%

CREATED_AT
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct1038198
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size79.9 MiB
2022-08-11T19:53:22.794
 
4
2022-08-12T10:00:17.378
 
4
2022-08-15T08:43:01.834
 
4
2022-08-11T10:00:16.488
 
4
2022-08-10T14:08:09.21
 
4
Other values (1038193)
1048555 

Length

Max length23
Median length23
Mean length22.886986
Min length19

Characters and Unicode

Total characters23998721
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1028510 ?
Unique (%)98.1%

Sample

1st row2022-07-20T17:17:27.569
2nd row2022-07-20T17:17:27.811
3rd row2022-07-20T17:17:41.301
4th row2022-07-20T17:17:49.666
5th row2022-07-20T17:17:54.482

Common Values

ValueCountFrequency (%)
2022-08-11T19:53:22.794 4
 
< 0.1%
2022-08-12T10:00:17.378 4
 
< 0.1%
2022-08-15T08:43:01.834 4
 
< 0.1%
2022-08-11T10:00:16.488 4
 
< 0.1%
2022-08-10T14:08:09.21 4
 
< 0.1%
2022-08-15T12:17:00.454 4
 
< 0.1%
2022-08-11T21:33:30.671 4
 
< 0.1%
2022-08-11T22:50:32.571 4
 
< 0.1%
2022-08-11T22:41:24.751 4
 
< 0.1%
2022-08-15T16:10:32.148 4
 
< 0.1%
Other values (1038188) 1048535
> 99.9%

Length

2023-04-04T19:05:46.444090image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2022-08-11t19:53:22.794 4
 
< 0.1%
2022-08-15t11:32:07.049 4
 
< 0.1%
2022-08-12t10:00:17.378 4
 
< 0.1%
2022-08-15t13:55:42.308 4
 
< 0.1%
2022-08-15t15:11:01.822 4
 
< 0.1%
2022-08-11t22:59:19.88 4
 
< 0.1%
2022-08-11t11:52:14.634 4
 
< 0.1%
2022-08-11t22:38:58.595 4
 
< 0.1%
2022-08-11t12:39:30.356 4
 
< 0.1%
2022-08-15t15:38:46.118 4
 
< 0.1%
Other values (1038188) 1048535
> 99.9%

Most occurring characters

ValueCountFrequency (%)
2 5040220
21.0%
0 3450246
14.4%
1 2251786
9.4%
- 2097150
8.7%
: 2097150
8.7%
3 1223650
 
5.1%
4 1216237
 
5.1%
5 1159734
 
4.8%
T 1048575
 
4.4%
. 1047505
 
4.4%
Other values (4) 3366468
14.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17708341
73.8%
Other Punctuation 3144655
 
13.1%
Dash Punctuation 2097150
 
8.7%
Uppercase Letter 1048575
 
4.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 5040220
28.5%
0 3450246
19.5%
1 2251786
12.7%
3 1223650
 
6.9%
4 1216237
 
6.9%
5 1159734
 
6.5%
6 910527
 
5.1%
7 881095
 
5.0%
8 869730
 
4.9%
9 705116
 
4.0%
Other Punctuation
ValueCountFrequency (%)
: 2097150
66.7%
. 1047505
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 2097150
100.0%
Uppercase Letter
ValueCountFrequency (%)
T 1048575
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22950146
95.6%
Latin 1048575
 
4.4%

Most frequent character per script

Common
ValueCountFrequency (%)
2 5040220
22.0%
0 3450246
15.0%
1 2251786
9.8%
- 2097150
9.1%
: 2097150
9.1%
3 1223650
 
5.3%
4 1216237
 
5.3%
5 1159734
 
5.1%
. 1047505
 
4.6%
6 910527
 
4.0%
Other values (3) 2455941
10.7%
Latin
ValueCountFrequency (%)
T 1048575
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23998721
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 5040220
21.0%
0 3450246
14.4%
1 2251786
9.4%
- 2097150
8.7%
: 2097150
8.7%
3 1223650
 
5.1%
4 1216237
 
5.1%
5 1159734
 
4.8%
T 1048575
 
4.4%
. 1047505
 
4.4%
Other values (4) 3366468
14.0%

UPDATED_AT
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct1046245
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size79.9 MiB
2022-10-12T22:00:00.182
 
4
2022-08-11T18:30:30.508
 
3
2022-08-15T13:33:14.997
 
3
2022-12-24T16:35:57.598
 
3
2022-08-11T11:52:14.708
 
3
Other values (1046240)
1048559 

Length

Max length23
Median length23
Mean length22.887402
Min length19

Characters and Unicode

Total characters23999158
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1043925 ?
Unique (%)99.6%

Sample

1st row2022-07-20T17:17:27.62
2nd row2022-07-20T17:17:27.866
3rd row2022-07-20T17:17:41.359
4th row2022-07-20T17:17:49.713
5th row2022-07-20T17:17:54.531

Common Values

ValueCountFrequency (%)
2022-10-12T22:00:00.182 4
 
< 0.1%
2022-08-11T18:30:30.508 3
 
< 0.1%
2022-08-15T13:33:14.997 3
 
< 0.1%
2022-12-24T16:35:57.598 3
 
< 0.1%
2022-08-11T11:52:14.708 3
 
< 0.1%
2022-10-12T23:00:00.112 3
 
< 0.1%
2022-10-12T18:00:00.112 3
 
< 0.1%
2022-05-23T22:20:06.174 3
 
< 0.1%
2022-08-26T18:00:00.05 3
 
< 0.1%
2022-08-22T19:38:03.881 2
 
< 0.1%
Other values (1046235) 1048545
> 99.9%

Length

2023-04-04T19:05:46.586160image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2022-10-12t22:00:00.182 4
 
< 0.1%
2022-10-12t23:00:00.112 3
 
< 0.1%
2022-08-11t18:30:30.508 3
 
< 0.1%
2022-05-23t22:20:06.174 3
 
< 0.1%
2022-10-12t18:00:00.112 3
 
< 0.1%
2022-08-26t18:00:00.05 3
 
< 0.1%
2022-08-11t11:52:14.708 3
 
< 0.1%
2022-12-24t16:35:57.598 3
 
< 0.1%
2022-08-15t13:33:14.997 3
 
< 0.1%
2022-08-15t13:14:25.938 2
 
< 0.1%
Other values (1046235) 1048545
> 99.9%

Most occurring characters

ValueCountFrequency (%)
2 5039537
21.0%
0 3454083
14.4%
1 2248470
9.4%
- 2097150
8.7%
: 2097150
8.7%
3 1222121
 
5.1%
4 1216125
 
5.1%
5 1160727
 
4.8%
T 1048575
 
4.4%
. 1047502
 
4.4%
Other values (4) 3367718
14.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17708781
73.8%
Other Punctuation 3144652
 
13.1%
Dash Punctuation 2097150
 
8.7%
Uppercase Letter 1048575
 
4.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 5039537
28.5%
0 3454083
19.5%
1 2248470
12.7%
3 1222121
 
6.9%
4 1216125
 
6.9%
5 1160727
 
6.6%
6 913817
 
5.2%
7 879531
 
5.0%
8 872658
 
4.9%
9 701712
 
4.0%
Other Punctuation
ValueCountFrequency (%)
: 2097150
66.7%
. 1047502
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 2097150
100.0%
Uppercase Letter
ValueCountFrequency (%)
T 1048575
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22950583
95.6%
Latin 1048575
 
4.4%

Most frequent character per script

Common
ValueCountFrequency (%)
2 5039537
22.0%
0 3454083
15.1%
1 2248470
9.8%
- 2097150
9.1%
: 2097150
9.1%
3 1222121
 
5.3%
4 1216125
 
5.3%
5 1160727
 
5.1%
. 1047502
 
4.6%
6 913817
 
4.0%
Other values (3) 2453901
10.7%
Latin
ValueCountFrequency (%)
T 1048575
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23999158
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 5039537
21.0%
0 3454083
14.4%
1 2248470
9.4%
- 2097150
8.7%
: 2097150
8.7%
3 1222121
 
5.1%
4 1216125
 
5.1%
5 1160727
 
4.8%
T 1048575
 
4.4%
. 1047502
 
4.4%
Other values (4) 3367718
14.0%

AMOUNT
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct5646
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.469001
Minimum1
Maximum20000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 MiB
2023-04-04T19:05:46.712057image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q17
median10
Q310
95-th percentile75.25
Maximum20000
Range19999
Interquartile range (IQR)3

Descriptive statistics

Standard deviation184.76429
Coefficient of variation (CV)6.7262834
Kurtosis1873.6826
Mean27.469001
Median Absolute Deviation (MAD)1
Skewness32.925593
Sum28803308
Variance34137.841
MonotonicityNot monotonic
2023-04-04T19:05:46.838506image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 361689
34.5%
9 253659
24.2%
7 138598
 
13.2%
5 71209
 
6.8%
4.25 25612
 
2.4%
15 24875
 
2.4%
2.5 22448
 
2.1%
50 11223
 
1.1%
13.5 10431
 
1.0%
100 9497
 
0.9%
Other values (5636) 119334
 
11.4%
ValueCountFrequency (%)
1 6
 
< 0.1%
1.15 1
 
< 0.1%
1.34 1
 
< 0.1%
1.43 1
 
< 0.1%
1.75 1
 
< 0.1%
2 3
 
< 0.1%
2.25 1
 
< 0.1%
2.46 3
 
< 0.1%
2.5 22448
2.1%
2.6 1
 
< 0.1%
ValueCountFrequency (%)
20000 2
< 0.1%
19905 1
< 0.1%
19575.56 1
< 0.1%
18000 1
< 0.1%
17000 1
< 0.1%
16871.54 1
< 0.1%
16295.88 1
< 0.1%
16267.51 1
< 0.1%
15886 1
< 0.1%
15306.02 1
< 0.1%

FEES
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct550
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49690479
Minimum0
Maximum450.01
Zeros5990
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size8.0 MiB
2023-04-04T19:05:46.975568image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.12
Q10.21
median0.23
Q30.25
95-th percentile2.3
Maximum450.01
Range450.01
Interquartile range (IQR)0.04

Descriptive statistics

Standard deviation1.2591194
Coefficient of variation (CV)2.5339249
Kurtosis20947.592
Mean0.49690479
Median Absolute Deviation (MAD)0.02
Skewness82.217872
Sum521041.94
Variance1.5853818
MonotonicityNot monotonic
2023-04-04T19:05:47.093086image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.23 345859
33.0%
0.21 211091
20.1%
0.25 95527
 
9.1%
0.12 62533
 
6.0%
0.17 35374
 
3.4%
0.35 19619
 
1.9%
0.06 18152
 
1.7%
0.2 17646
 
1.7%
2.28 17322
 
1.7%
0.18 16100
 
1.5%
Other values (540) 209352
20.0%
ValueCountFrequency (%)
0 5990
 
0.6%
0.02 23
 
< 0.1%
0.03 3
 
< 0.1%
0.04 10
 
< 0.1%
0.05 8
 
< 0.1%
0.06 18152
1.7%
0.07 4315
 
0.4%
0.08 14
 
< 0.1%
0.09 12
 
< 0.1%
0.1 11636
1.1%
ValueCountFrequency (%)
450.01 1
 
< 0.1%
220.33 1
 
< 0.1%
195.31 6
< 0.1%
193.45 1
 
< 0.1%
177.73 1
 
< 0.1%
160.3 1
 
< 0.1%
97.66 3
< 0.1%
56.26 2
 
< 0.1%
48.79 3
< 0.1%
44.11 1
 
< 0.1%

RETAILER_CUT
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct396
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.23433452
Minimum0
Maximum125.1
Zeros6582
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size8.0 MiB
2023-04-04T19:05:47.212397image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.06
Q10.11
median0.13
Q30.15
95-th percentile0.93
Maximum125.1
Range125.1
Interquartile range (IQR)0.04

Descriptive statistics

Standard deviation0.44448852
Coefficient of variation (CV)1.8968119
Kurtosis8025.9584
Mean0.23433452
Median Absolute Deviation (MAD)0.02
Skewness41.577119
Sum245717.32
Variance0.19757004
MonotonicityNot monotonic
2023-04-04T19:05:47.339360image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.13 369698
35.3%
0.11 185180
17.7%
0.15 125153
 
11.9%
0.06 65147
 
6.2%
0.09 34863
 
3.3%
0.07 25003
 
2.4%
0.19 18721
 
1.8%
0.03 18193
 
1.7%
0.61 17577
 
1.7%
2 17150
 
1.6%
Other values (386) 171890
16.4%
ValueCountFrequency (%)
0 6582
 
0.6%
0.03 18193
 
1.7%
0.04 4257
 
0.4%
0.05 2708
 
0.3%
0.06 65147
 
6.2%
0.07 25003
 
2.4%
0.08 637
 
0.1%
0.09 34863
 
3.3%
0.1 16318
 
1.6%
0.11 185180
17.7%
ValueCountFrequency (%)
125.1 1
 
< 0.1%
61.25 1
 
< 0.1%
54.29 6
< 0.1%
53.77 1
 
< 0.1%
49.4 1
 
< 0.1%
44.56 1
 
< 0.1%
27.14 3
< 0.1%
15.64 2
 
< 0.1%
13.56 3
< 0.1%
12.26 1
 
< 0.1%

STATUS
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.1 MiB
SUCCESSFUL
1032957 
PROVIDER_FAILED
 
8125
BALANCE_FAILED
 
6695
CANCELED
 
670
NOT_PROCESSED_FAILED
 
116
Other values (3)
 
12

Length

Max length20
Median length10
Mean length10.064159
Min length8

Characters and Unicode

Total characters10553026
Distinct characters19
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSUCCESSFUL
2nd rowSUCCESSFUL
3rd rowSUCCESSFUL
4th rowSUCCESSFUL
5th rowSUCCESSFUL

Common Values

ValueCountFrequency (%)
SUCCESSFUL 1032957
98.5%
PROVIDER_FAILED 8125
 
0.8%
BALANCE_FAILED 6695
 
0.6%
CANCELED 670
 
0.1%
NOT_PROCESSED_FAILED 116
 
< 0.1%
CLIENT_PENDING 7
 
< 0.1%
CONFIRM_PENDING 4
 
< 0.1%
CANCEL_FAILED 1
 
< 0.1%

Length

2023-04-04T19:05:47.455669image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-04T19:05:47.573921image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
successful 1032957
98.5%
provider_failed 8125
 
0.8%
balance_failed 6695
 
0.6%
canceled 670
 
0.1%
not_processed_failed 116
 
< 0.1%
client_pending 7
 
< 0.1%
confirm_pending 4
 
< 0.1%
cancel_failed 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
S 3099103
29.4%
C 2074078
19.7%
U 2065914
19.6%
E 1064305
 
10.1%
L 1055267
 
10.0%
F 1047898
 
9.9%
A 28998
 
0.3%
D 23859
 
0.2%
I 23084
 
0.2%
R 16370
 
0.2%
Other values (9) 54150
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 10537962
99.9%
Connector Punctuation 15064
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 3099103
29.4%
C 2074078
19.7%
U 2065914
19.6%
E 1064305
 
10.1%
L 1055267
 
10.0%
F 1047898
 
9.9%
A 28998
 
0.3%
D 23859
 
0.2%
I 23084
 
0.2%
R 16370
 
0.2%
Other values (8) 39086
 
0.4%
Connector Punctuation
ValueCountFrequency (%)
_ 15064
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10537962
99.9%
Common 15064
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 3099103
29.4%
C 2074078
19.7%
U 2065914
19.6%
E 1064305
 
10.1%
L 1055267
 
10.0%
F 1047898
 
9.9%
A 28998
 
0.3%
D 23859
 
0.2%
I 23084
 
0.2%
R 16370
 
0.2%
Other values (8) 39086
 
0.4%
Common
ValueCountFrequency (%)
_ 15064
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10553026
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 3099103
29.4%
C 2074078
19.7%
U 2065914
19.6%
E 1064305
 
10.1%
L 1055267
 
10.0%
F 1047898
 
9.9%
A 28998
 
0.3%
D 23859
 
0.2%
I 23084
 
0.2%
R 16370
 
0.2%
Other values (9) 54150
 
0.5%

TOTAL_AMOUNT_INCLUDING_TAX
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct5732
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.68107
Minimum1
Maximum20000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 MiB
2023-04-04T19:05:47.695110image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q19
median10
Q310
95-th percentile95.72
Maximum20000
Range19999
Interquartile range (IQR)1

Descriptive statistics

Standard deviation185.00397
Coefficient of variation (CV)6.4503857
Kurtosis1863.1206
Mean28.68107
Median Absolute Deviation (MAD)1
Skewness32.780872
Sum30074253
Variance34226.468
MonotonicityNot monotonic
2023-04-04T19:05:47.823786image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 417944
39.9%
9 257269
24.5%
5 68177
 
6.5%
7 52029
 
5.0%
15 28029
 
2.7%
10.01 26839
 
2.6%
4.25 25612
 
2.4%
2.5 22448
 
2.1%
50 11482
 
1.1%
13.5 10430
 
1.0%
Other values (5722) 128316
 
12.2%
ValueCountFrequency (%)
1 6
 
< 0.1%
1.15 1
 
< 0.1%
1.34 1
 
< 0.1%
1.43 1
 
< 0.1%
1.75 1
 
< 0.1%
2 3
 
< 0.1%
2.25 1
 
< 0.1%
2.46 3
 
< 0.1%
2.5 22448
2.1%
2.6 1
 
< 0.1%
ValueCountFrequency (%)
20000 2
< 0.1%
19905 1
< 0.1%
19575.56 1
< 0.1%
18000 1
< 0.1%
17000 1
< 0.1%
16871.54 1
< 0.1%
16295.88 1
< 0.1%
16267.51 1
< 0.1%
15886 1
< 0.1%
15306.02 1
< 0.1%

TOTAL_AMOUNT_PAID
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct6826
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.899008
Minimum1
Maximum20000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 MiB
2023-04-04T19:05:47.961359image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q19
median10
Q310
95-th percentile98.42
Maximum20000
Range19999
Interquartile range (IQR)1

Descriptive statistics

Standard deviation185.23041
Coefficient of variation (CV)6.4095766
Kurtosis1854.3742
Mean28.899008
Median Absolute Deviation (MAD)1
Skewness32.680721
Sum30302778
Variance34310.305
MonotonicityNot monotonic
2023-04-04T19:05:48.087272image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 417862
39.9%
9 257044
24.5%
5 68157
 
6.5%
7 52025
 
5.0%
15 27620
 
2.6%
10.01 26838
 
2.6%
4.25 25611
 
2.4%
2.5 22448
 
2.1%
13.5 10435
 
1.0%
25 8393
 
0.8%
Other values (6816) 132142
 
12.6%
ValueCountFrequency (%)
1 1
 
< 0.1%
1.15 1
 
< 0.1%
1.34 1
 
< 0.1%
1.43 1
 
< 0.1%
2 3
 
< 0.1%
2.25 1
 
< 0.1%
2.46 3
 
< 0.1%
2.5 22448
2.1%
3 3
 
< 0.1%
3.3 1
 
< 0.1%
ValueCountFrequency (%)
20000 2
< 0.1%
19905 1
< 0.1%
19575.56 1
< 0.1%
18000 1
< 0.1%
17000 1
< 0.1%
16871.54 1
< 0.1%
16295.88 1
< 0.1%
16267.51 1
< 0.1%
15886 1
< 0.1%
15306.02 1
< 0.1%

WALLET_BALANCE_BEFORE_TRANSACTION
Real number (ℝ)

Distinct275562
Distinct (%)26.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1007.0043
Minimum-14324.51
Maximum32983.59
Zeros38
Zeros (%)< 0.1%
Negative188
Negative (%)< 0.1%
Memory size8.0 MiB
2023-04-04T19:05:48.218013image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-14324.51
5-th percentile84.37
Q1409.51
median806.94
Q31306.73
95-th percentile2625.903
Maximum32983.59
Range47308.1
Interquartile range (IQR)897.22

Descriptive statistics

Standard deviation1009.167
Coefficient of variation (CV)1.0021477
Kurtosis90.879145
Mean1007.0043
Median Absolute Deviation (MAD)435.64
Skewness5.7899016
Sum1.0559195 × 109
Variance1018418
MonotonicityNot monotonic
2023-04-04T19:05:48.338900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
375.28 58
 
< 0.1%
500 55
 
< 0.1%
1000 52
 
< 0.1%
0 38
 
< 0.1%
308.41 33
 
< 0.1%
773.13 28
 
< 0.1%
300 21
 
< 0.1%
340.14 20
 
< 0.1%
14.11 20
 
< 0.1%
66.34 20
 
< 0.1%
Other values (275552) 1048230
> 99.9%
ValueCountFrequency (%)
-14324.51 1
 
< 0.1%
-9017.29 2
< 0.1%
-7017.57 3
< 0.1%
-2998.42 1
 
< 0.1%
-2996.53 2
< 0.1%
-2995.24 1
 
< 0.1%
-2988.42 2
< 0.1%
-2971.22 2
< 0.1%
-2842.06 1
 
< 0.1%
-2579.18 1
 
< 0.1%
ValueCountFrequency (%)
32983.59 1
< 0.1%
32961.92 1
< 0.1%
32718.51 1
< 0.1%
32713.33 1
< 0.1%
32706.43 1
< 0.1%
32697.56 1
< 0.1%
32688.69 1
< 0.1%
32684.5 1
< 0.1%
32657.83 1
< 0.1%
32648.96 1
< 0.1%

MAIN_SYSTEM_ID
Real number (ℝ)

Distinct4944
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50529.554
Minimum102
Maximum156199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 MiB
2023-04-04T19:05:48.460859image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum102
5-th percentile7102
Q119158
median40311
Q373267
95-th percentile122403
Maximum156199
Range156097
Interquartile range (IQR)54109

Descriptive statistics

Standard deviation37722.2
Coefficient of variation (CV)0.74653737
Kurtosis-0.026037972
Mean50529.554
Median Absolute Deviation (MAD)24415
Skewness0.88151721
Sum5.2984027 × 1010
Variance1.4229644 × 109
MonotonicityNot monotonic
2023-04-04T19:05:48.585055image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39409 2264
 
0.2%
14163 2129
 
0.2%
15200 1747
 
0.2%
49687 1711
 
0.2%
30067 1706
 
0.2%
18091 1665
 
0.2%
40758 1624
 
0.2%
17611 1601
 
0.2%
54819 1573
 
0.2%
36543 1572
 
0.1%
Other values (4934) 1030983
98.3%
ValueCountFrequency (%)
102 209
< 0.1%
115 174
< 0.1%
122 1
 
< 0.1%
193 165
< 0.1%
247 117
 
< 0.1%
248 74
 
< 0.1%
275 64
 
< 0.1%
290 301
< 0.1%
296 215
< 0.1%
300 299
< 0.1%
ValueCountFrequency (%)
156199 81
 
< 0.1%
156063 35
 
< 0.1%
155983 4
 
< 0.1%
155952 33
 
< 0.1%
155936 3
 
< 0.1%
155917 399
< 0.1%
155894 320
< 0.1%
155803 131
 
< 0.1%
155673 50
 
< 0.1%
155667 131
 
< 0.1%

Interactions

2023-04-04T19:05:41.714748image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:32.181283image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:33.550879image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:34.930117image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:36.232652image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:37.573807image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:38.967672image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:40.354229image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:41.892113image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:32.347916image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:33.718302image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:35.090945image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:36.398638image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:37.745921image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:39.142579image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:40.523415image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:42.075641image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:32.522209image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:33.896419image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:35.257314image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:36.573367image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:37.927827image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:39.321058image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:40.698620image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:42.247779image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:32.681031image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:34.062697image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:35.407894image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:36.727013image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:38.092152image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:39.484851image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:40.860076image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:42.423034image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:32.851751image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:34.235970image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:35.569088image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:36.892484image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:38.265179image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:39.655502image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:41.028303image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:42.608537image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:33.030696image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:34.413146image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:35.738548image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:37.066960image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:38.445031image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:39.831999image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:41.206638image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:42.794818image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:33.209040image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:34.590446image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:35.909386image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:37.242584image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:38.624605image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:40.009107image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:41.379745image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:42.965368image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:33.375985image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:34.753759image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:36.062430image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:37.399674image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:38.788316image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:40.173687image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-04T19:05:41.536435image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-04-04T19:05:48.697072image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
IDAMOUNTFEESRETAILER_CUTTOTAL_AMOUNT_INCLUDING_TAXTOTAL_AMOUNT_PAIDWALLET_BALANCE_BEFORE_TRANSACTIONMAIN_SYSTEM_IDSTATUS
ID1.0000.093-0.087-0.1610.0940.0940.0290.0300.018
AMOUNT0.0931.0000.8330.7820.8450.8440.047-0.0070.012
FEES-0.0870.8331.0000.9480.8540.8550.022-0.0100.018
RETAILER_CUT-0.1610.7820.9481.0000.8730.8740.021-0.0030.018
TOTAL_AMOUNT_INCLUDING_TAX0.0940.8450.8540.8731.0000.9990.0480.0080.012
TOTAL_AMOUNT_PAID0.0940.8440.8550.8740.9991.0000.0480.0080.013
WALLET_BALANCE_BEFORE_TRANSACTION0.0290.0470.0220.0210.0480.0481.000-0.0220.059
MAIN_SYSTEM_ID0.030-0.007-0.010-0.0030.0080.008-0.0221.0000.008
STATUS0.0180.0120.0180.0180.0120.0130.0590.0081.000

Missing values

2023-04-04T19:05:43.441241image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-04T19:05:44.443964image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IDCREATED_ATUPDATED_ATAMOUNTFEESRETAILER_CUTSTATUSTOTAL_AMOUNT_INCLUDING_TAXTOTAL_AMOUNT_PAIDWALLET_BALANCE_BEFORE_TRANSACTIONMAIN_SYSTEM_ID
0213216682022-07-20T17:17:27.5692022-07-20T17:17:27.627.00.230.13SUCCESSFUL10.010.01017.1178550
1213216692022-07-20T17:17:27.8112022-07-20T17:17:27.86610.00.230.13SUCCESSFUL10.010.01079.96116836
2213216972022-07-20T17:17:41.3012022-07-20T17:17:41.3599.00.210.11SUCCESSFUL9.09.0167.5128799
3213217182022-07-20T17:17:49.6662022-07-20T17:17:49.7139.00.210.11SUCCESSFUL9.09.0481.4887876
4213217282022-07-20T17:17:54.4822022-07-20T17:17:54.5319.00.210.11SUCCESSFUL9.09.0639.5818899
5247344992022-08-11T09:52:00.3522022-08-11T09:52:00.40210.00.230.13SUCCESSFUL10.010.02841.208364
6247436292022-08-11T11:11:00.3392022-08-11T11:11:00.39410.00.230.13SUCCESSFUL10.010.0395.3793466
7247436402022-08-11T11:11:07.3692022-08-11T11:11:07.36985.55.002.00SUCCESSFUL85.590.5341.0861066
8311585482022-09-17T05:51:21.3592022-09-17T05:51:21.40810.00.230.12SUCCESSFUL10.010.0297.5514496
9180642862022-06-27T12:50:09.7192022-06-27T12:50:09.87.00.230.13SUCCESSFUL10.010.01443.0814940
IDCREATED_ATUPDATED_ATAMOUNTFEESRETAILER_CUTSTATUSTOTAL_AMOUNT_INCLUDING_TAXTOTAL_AMOUNT_PAIDWALLET_BALANCE_BEFORE_TRANSACTIONMAIN_SYSTEM_ID
104856587121382022-04-10T17:57:12.8392022-04-10T17:57:12.8810.000.250.15SUCCESSFUL10.0010.00795.325027
104856687127452022-04-10T18:03:18.4452022-04-10T18:03:18.49510.000.250.15SUCCESSFUL10.0010.001988.8140973
104856787127552022-04-10T18:03:25.4812022-04-10T18:03:25.53510.000.250.15SUCCESSFUL10.0010.002715.3025741
104856887127642022-04-10T18:03:35.0432022-04-10T18:03:35.09510.000.250.15SUCCESSFUL10.0010.00291.5239481
1048569103690172022-04-25T23:32:41.4142022-04-25T23:32:41.4615.000.380.22SUCCESSFUL15.0015.001449.1050801
1048570103690242022-04-25T23:32:42.532022-04-25T23:32:42.59310.000.250.15SUCCESSFUL10.0010.00481.2439409
104857187112822022-04-10T17:48:54.0392022-04-10T17:48:54.12610.000.250.15SUCCESSFUL10.0010.00216.5226615
104857287112832022-04-10T17:48:54.7322022-04-10T17:48:54.817.000.180.10SUCCESSFUL7.007.001515.9150900
104857387112842022-04-10T17:48:55.1132022-04-10T17:48:55.18510.000.250.15SUCCESSFUL10.0010.00570.6931528
104857487112852022-04-10T17:48:55.9242022-04-10T17:48:55.9954.250.110.06SUCCESSFUL4.254.251140.5229277